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We propose an empirical mode decomposition (EMD-) based method to extract features from the multichannel recordings of local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception.
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We conducted the object-based method to extract particles from images.
Second, due to the absence of materials properties in DBpedia, we use an ontology-based method to extract properties knowledge from the HTML tables of corresponding Wikipedia Web pages for enriching MMKG.
Akbarya and bishnoi [32] used wavelet-based method to extract features and binary decision tree to classify them.
We approached this detection problem by employing a template-matching-based method to extract the morphological information from EEG signals first, and then applying a linear discriminant function (LDF) to the features selected for pattern classification.
Recently, Chen et al. [ 9] developed with GMPS a count-based method to extract phase information from the regional LV count changes throughout the cardiac cycle.
The ADESSA system, a real-time decision support service for detecting or monitoring adverse drug events, uses a regular expression and dictionary-based method to extract ADRs [ 15, 16].
More recently, Sohn et al. [ 22] used a rule-based method to extract physician-asserted drug side-effects from clinical narratives of psychiatry and psychology patients and Liu et al. [ 23] examined the use of retrospective medication orders and inpatient laboratory results documented in the EHRs to identify ADRs.
Most systems for detecting drug effects from texts use simple dictionary based methods to recognize the entities and pattern-based approaches to extract the relations between them.
First, we used feature selection based on a correlation coefficient method to extract highly discriminative regions and construct the optimal feature set for classification.
ICA is a data driven method to extract factors based on high order statistics of data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com